9 Differential Expression
We then compare the protein expression between strains for each pairwise comparison (28 unique pairwise combinations)
To detect differentially expressed genes, we use the limma analysis on normalized protein expression.
9.1 Volcano plots
First, we can look at the volcano plots (log-foldchange vs qvalue) for each unique pairwise comparison.
#ind_na_rows = find_na_rows(int_norm,as.indices = T)
df_imputed = tibble( uniprot = int_norm_ids,
is_imputed = (rowSums( is.na(int_norm))>0)* 1,
imputed = factor(is_imputed, levels = c(0,1), labels = c("not", "is_imputed")))
volcPlot(INPUT=int_bpca, IMPUTED=df_imputed, MIN_LFC=2, MIN_PVAL=0.01, WHICH='both', TOPN = 20, plot = F, use_plotly = T)
#> Joining, by = c("ID", "pValue", "qValue", "EffectSize",
#> "comparison", "sig", "log10_qvalue", "SGD", "ORF",
#> "UNIPROT", "GENENAME", "is_imputed", "imputed")